In today's information-driven society, organizations are collecting and accumulating more data than ever before. Databases are growing and within a couple of years, the world's largest database is likely to be several petabytes in size. As databases grow in size, their performance generally degrades. Their availability is also often reduced because disaster recovery and routine maintenance tasks (e.g., backup, reorganization), some of which may require the databases to be taken offline, take much longer. Moreover, although computer technology has improved dramatically to enable ever larger databases, the cost and complexity of managing such databases have not kept pace so that the task of managing the databases is increasingly taxing on the already stretched information technology staff and budget.
In practice, the data stored in the databases typically have different activity profiles and value to the organization. If each piece of data were to be managed in accordance with its activity profile and value to the organization, the cost and complexity of managing the data would be significantly reduced.
In view of the foregoing, there is a need for a more efficient and intelligent method of managing database data, including archiving and retrieving data.